Over the last decade, the biomedical research community has published and made available, on the Web, several sources consisting of biomedical data and knowledge: anonymized medical records [1]}, imaging data [2]}, sequencing data [3]}, biomedical publications [4]}, biological assays [5]}, chemical compounds and their activities [6]}, biological molecules and their characteristics [7]}, knowledge encoded in biological pathways [8]}, [9]}, animal models [10]}, drugs and their protein targets [11]}, medical knowledge on organs, symptoms, diseases, and adverse reactions [12]}, etc. However, biomedical researchers still face severe logistical and technical challenges to integrate, analyze, and visualize heterogeneous data and knowledge from these diverse and often isolated sources. Researchers need to be aware of the availability and accessibility of these sources on the Web, and need extensive computational skills and knowledge to query and explore them. To systematically consume and integrate the data and knowledge from these sources for use in their analyses, researchers often spend a significant amount of their time dealing with the heterogeneous syntaxes, structures, formats, schemas, and biomedical entity notations used across these sources.
